PURPOSE
library(plotly)
library(ggplot2)
library(ggvis)
library(googleVis)
library(rCharts)
EXAMPLES
1. ggplot2

Pros
- super-control over plot features and format
- uses grammar of graphics
- lots of usage and support
- good skill level already attained, with lots of home-made examples
Cons
- no further ‘official’ development - basically abandoned in favour of ggvis
- no built-in interactivity
- can be cumbersome code
- can’t easily download
Use
- static charts, including R Markdown HTML files
- maybe with Shiny, but probably choose alternatives below
2. ggvis
Pros
- designed for more interactivity than ggplot2 (although require shiny)
- defaults to cleaner style than ggplot2
- takes advantage of pipe (%>%) and integrates with dplyr
- easy export/download
Cons
- interactivity requires Shiny
3. Plotly
Pros
- full-on library of interactive charts
- can embed charts with some interactivity in R Markdown HTML files
Cons
- interactivity is limited to zooming, hover labels (which is cool but not sufficient)
- for shiny, requires library on shiny server - compatible version
- recent update (v4.0) not backwards compatible, breaking older charts
Use
- embed in R Markdown HTML for limited interactivity
- possibly with shiny
4. Google Viz
Pros
- Well-developed Google chart library
- Basic interaction (hover labels, item selection) built-in
Cons
- displays in browser only, not R plot window or R Viewer
- doesn’t directly integrate with R Markdown HTML docs - can be fidgetly to get it to work, only then it only displays in browser - not R Markdown preview
- various charts may have limited customization options for presentation
Use
- further research but could work in R Markdown docs AND shiny
5. rCharts
Pros
- collection of chart libraries, including rPlot that is inspired by ggplot2-style
- works well with R Markdown
- basic interactivity (hover labels)
Cons
- the chart libraries may not be super-flexible, in terms of available presentation options
- not super-thorough documentation
- more complex interactions may required Javascript coding
Use
- tbd with more research but could work for R Markdown and shiny
- (similar to Google Vis)
Conclusion
- ggplot for exploration within R and for static charts in R markdown
- get up to speed with more ggvis, as it is likely future of R graphics
- get up to speed on shiny + ggvis
- keep plotly in mind for situations that may arise
- experiment with Google Vis and rCharts - esp with R Markdown docs